/*******************************************************************************
* Copyright (c) 2015-2017
* School of Electrical, Computer and Energy Engineering, Arizona State University
* PI: Prof. Shimeng Yu
* All rights reserved.
*   
* This source code is part of NeuroSim - a device-circuit-algorithm framework to benchmark 
* neuro-inspired architectures with synaptic devices(e.g., SRAM and emerging non-volatile memory). 
* Copyright of the model is maintained by the developers, and the model is distributed under 
* the terms of the Creative Commons Attribution-NonCommercial 4.0 International Public License 
* http://creativecommons.org/licenses/by-nc/4.0/legalcode.
* The source code is free and you can redistribute and/or modify it
* by providing that the following conditions are met:
*   
*  1) Redistributions of source code must retain the above copyright notice,
*     this list of conditions and the following disclaimer. 
*   
*  2) Redistributions in binary form must reproduce the above copyright notice,
*     this list of conditions and the following disclaimer in the documentation
*     and/or other materials provided with the distribution.
*   
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
* ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
* 
* Developer list: 
*   Pai-Yu Chen     Email: pchen72 at asu dot edu 
*                     
*   Xiaochen Peng   Email: xpeng15 at asu dot edu
********************************************************************************/

#ifndef FORMULA_H_
#define FORMULA_H_

#include <vector>

double sigmoid(double x);
double truncate(double x, int numBit, double threshold=0.5);
double round_th(double x, double threshold);
double NonlinearWeight(double xPulse, int maxNumLevel, double A, double B, double minConductance);
double InvNonlinearWeight(double conductance, int maxNumLevel, double A, double B, double minConductance);
double MeasuredLTP(double xPulse, int maxNumLevel, std::vector<double>& dataConductanceLTP);
double MeasuredLTD(double xPulse, int maxNumLevel, std::vector<double>& dataConductanceLTD);
double InvMeasuredLTP(double conductance, int maxNumLevel, std::vector<double>& dataConductanceLTP);
double InvMeasuredLTD(double conductance, int maxNumLevel, std::vector<double>& dataConductanceLTD);
double getParamA(double NL);
double NonlinearConductance(double C, double NL, double Vw, double Vr, double V);

#endif
